This invention pertains to the field of artificial intelligence, and more particularly, to an apparatus for an ontology engine that is usable by any reasoning system that has requirements for obtaining an evaluation of questions.
For many organizations, their most valuable asset is their knowledge. More and more companies are embracing knowledge-based system technology as a means of putting the right knowledge in the hands of the right people at the right time. Intelligent electronic manuals, internal help desks, customer support hotlines, call avoidance troubleshooting software, pre-sales assessment, configuration guides, and proactive system management are some of the ways that the emerging field of computer aided decision support allows people to perform critical tasks and make key decisions with the skill of the leading experts, but without the labor costs of employing experts in delivering the support services.
Generally, computer-assisted solutions entail the employment of a decision support system, or a system that employs a knowledge representation paradigm to determine a solution to a problem. A knowledge representation paradigm comprises two components: a knowledge representation fact base and a reasoning system. A knowledge representation fact base is the means by which an expert""s knowledge is encoded into a knowledge base, or a repository of information needed to solve a problem. A knowledge representation fact base can be thought of as comprising a number of related components called knowledge objects, where each object exists in a specific relationship to other objects in the knowledge representation fact base. Objects in a knowledge representation fact base represent the different components into which actual facts in the real world can be remembered and used. The specific terminology and definitions for concepts used in a given knowledge representation fact base are referred to as the knowledge representation paradigm""s ontology: the terms knowledge representation fact base and ontology are often used interchangeably.
A reasoning system is the logic underlying a decision support system. A reasoning system of a decision support system is the logic component of the decision support system, as opposed to the knowledge repository component. A reasoning system is the mechanism that uses a knowledge representation model to make inferences between objects in the knowledge representation fact base. During a decision support session, a knowledge representation paradigm is employed to find a solution to problem. A reasoning system of the knowledge representation paradigm invokes an algorithm using its knowledge representation model to operate on knowledge in the system""s knowledge representation fact base. A knowledge representation model can be a tree, or a graph, for example. Oftentimes, the terms knowledge representation paradigm and reasoning system are used synonymously. In large part, this is because in current systems, a knowledge representation fact base is inseparably part of a corresponding reasoning system: a knowledge representation fact base has semantics that are usually closely tied to a reasoning system algorithms.
Examples of existing knowledge representation paradigms include basic text, decision trees, decision graphs, case-bases, default logic, augmented fault, and Bayesian belief networks. A new decision support system using incremental conclusions is the subject of the aforementioned co-pending patent application. In a decision tree knowledge representation paradigm, for example, the knowledge representation fact base is composed of a number of question and answer knowledge objects, wherein knowledge is encoded into a knowledge base in the form of question and answer tests. The reasoning system of the decision tree knowledge representation paradigm comprises an algorithm of IF-THEN-ELSE statements that operate on the knowledge objects to create a tree model. The tree model encapsulates a series of decision flows that can be made using the question and answer objects in a decision support session. The logic of the IF-THEN-ELSE statements then dictate the relationship between these objects. Just as there are many different programming languages, each best suited for certain types of projects (e.g., FORTRAN for number crunching, Visual Basic for GUI development, C++ for object-oriented solutions), there are also many different ways for representing knowledge, some more appropriate for a given problem domain (i.e. subject matter) than others.
One example of a currently available decision support system is Hewlett-Packard""s internal KnowledgeTrees system used for customer support. The knowledge representation paradigm in KnowledgeTrees uses a question-answer decision graph model to make inferences between question-answer objects in its fact base. In KnowledgeTrees, a question is directly associated with its answers. A question contains the answers within the same question data structure called the Q-node. Its inherent assumption is that the answers are mutually exclusive. The same Q-Node contains the control that links each answer to one subsequent question. There are several drawbacks to the KnowledgeTrees system. The simplicity of the Q-node knowledge representation limits the reusability of the questions and answers in other parts of the decision support system. This representation also excludes the ability for other reasoning systems to use its knowledge representation fact base.
ServiceWare""s Knowledge Pak system is a symptom-case-question knowledge representation fact base. The Knowledge Pak system is described as being reasoning system neutral, but is conveniently designed for a case-based reasoning system. The logical control of the questions are tied to a case-based approach which tends to make alternative logic control of the decision support system difficult (i.e., in practice, it does not easily support the use of other reasoning systems). This system does not support non-exclusive answers to questions, nor are multiple conclusions possible during a decision support session. The Knowledge Pak of Questions, Solutions, Symptoms, and Cases are separate objects in Knowledge Pak, increasing its knowledge authoring complexity. Furthermore, there is no inherent persistent question support for reuse of discovered information.
Logica""s AdvantageKbs is a decision support product that uses a symptom, cause, test, and solution ontology. Such a system requires maintenance from specially trained knowledge experts. While technically it offers good diagnostic troubleshooting, authoring by a wide variety of experts is not advised by the company due to its complexity. The ontology is not usable for other decision support needs such as configuration, analysis, nor provisioning. Their ability to automate the questions is also limited.
lnference""s CBR Express is a decision support system based on a case-based knowledge representation paradigm that builds cases for diagnostic situations. The underlying knowledge representation consists of weighted questions that aim to distinguish a case or hypothesis from all others in the case base. The questions and answers, while exportable, are not shared with other case-bases. Cross domain references are impossible, as is the possibility of including all domains in a single casebase due to cognitive scalability issues. The CBR representation also includes answer xe2x80x9cweightsxe2x80x9d which make the representation very difficult to maintain and understand.
This invention addresses some of the deficiencies of currently available knowledge representation paradigms. In existing paradigms, a knowledge representation fact base is inseparably part of a corresponding reasoning system as seen in the examples above. Proprietary knowledge representation can substantially limit the ability to reuse and leverage knowledge that could otherwise be used by other sources such as external data gathering knowledge agents. Current solutions do not provide the system supporting the ontology to maintain and reassert the dynamic knowledge learned during a decision support session. Furthermore, many problem domains, particularly complex ones, are best handled by a reasoning system that uses a combination of knowledge representation paradigms to optimize the quantity and quality of information, also called multi-modal reasoning capabilities. This is not possible, however, when the representation of knowledge is inseparably part of any one reasoning system. For example, a decision tree knowledge representation paradigm comprises a reasoning system that operates on questions and answers (objects) to create a tree hierarchy (model). These knowledge objects could not be used by a decision graph reasoning system, which operates and expects a series of like objects that can be placed in a decision graph model.
An intuitive and flexible knowledge authoring environment should be approachable by a wide variety of experts (also known as knowledge authors), to achieve comprehensive knowledge bases. The complexity of any given knowledge representation paradigm can be a use-inhibiting factor. When the content requirements of a knowledge-based system require a large population of authors, the knowledge representation paradigm must be easy to use so as to be accessible by a wide variety of experts. If a knowledge representation paradigm is too complex, more authorship skills are required, more training is required, and knowledge authorship is limited. However, when a knowledge representation is too simplistic, the ability for machine-assisted reasoning is severely limited. Computer aided decision support works best when the knowledge representation has enough structure for automated reasoning. As a result, a critical balance must be struck between simplicity of the knowledge representation that encourages wide authorship by experts, and the depth of representation necessary to make the knowledge usable by a knowledge reasoning system.
A more useful knowledge representation paradigm should have the ability to simulate the reasoning of a human being. For a given question, and a given problem, multiple answers and solutions are always possible. As a result, a reasoning system should have the ability to handle multiple answers to a question and to generate multiple conclusions. Furthermore, a knowledge representation paradigm should support flexible integration of knowledge agents. A knowledge agent is, commonly, a method by which a problem component, such as a question, or a task, is solved. Currently, knowledge representation paradigms are limited to the methods by which problem components are resolved.
A need exists, therefore, for an apparatus to address these concerns.
This invention is, in general, directed to an apparatus for a multi-modal reasoning ontology engine.
Thus, the invention may comprise an apparatus for a multi-modal reasoning ontology engine, comprising at least one computer readable medium; and a data structure embedded on the computer readable medium, comprising a knowledge representation fact base of knowledge objects compatible with reasoning systems requiring an evaluation of questions, comprising at least one task, and at least one result for a given one of the at least one task, wherein each of the results corresponds to a given one of the tasks to form a unique task-result pair.
Thus, the invention may further comprise an apparatus for a multi-modal reasoning ontology engine, comprising at least one computer readable medium; and a data structure embedded on the computer readable medium, comprising a knowledge representation fact base of knowledge objects compatible with reasoning systems requiring an evaluation of questions, comprising at least one task, and at least one result for a given one of the at least one task, wherein each of the results corresponds to a given one of the tasks to form a unique task-result pair; and a premise maintenance system of truth objects, wherein the truth objects are derived from the knowledge objects by a reasoning system, and the premise maintenance system infers knowledge from the knowledge objects.